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[mpi] vector, test and draft of doc
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doc/reference/c++/mpi/CMakeLists.txt
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doc/reference/c++/mpi/CMakeLists.txt
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# Doxygen sources
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#set_property(GLOBAL APPEND PROPERTY DOXYGEN_SOURCES ${TRIQS_SOURCE_DIR}/triqs/mpi/mpi.hpp)
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all_tests()
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152
doc/reference/c++/mpi/mpi.rst
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152
doc/reference/c++/mpi/mpi.rst
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MPI
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===============
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.. warning::
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Library of beta quality.
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More functionality may be added in the future.
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Introduction
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--------------
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The purpose of the MPi library is to provide a simplified, C++-style API to the MPI routines for standard types
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(those for which an MPI type exists) and for composite higher-level objects, in particular the TRIQS arrays and Green's functions.
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The communication routines in the C API of the MPI library have require several parameters, such as the ``reduce`` operation:
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.. code-block:: c
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int MPI_Reduce(void *sendbuf, void *recvbuf, int count,
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MPI_Datatype datatype, MPI_Op op, int root, MPI_Comm comm)
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In principle, all parameters except for the communicator and id of the root process can be determined from the variable or object to be transmitted.
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In most cases, we use ``MPI_COMM_WORLD`` as the communicator, take the id 0 for the root process and use ``MPI_SUM`` as the operation.
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This allows us to write
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.. code-block:: c
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int a = 5;
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triqs::mpi::reduce(a);
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Such an interface is simpler to use and much less error prone. For higher-level objects, such as vectors or higher-dimensional arrays, the simplifcation is even more significant. Take the scatter and gather operations as examples:
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.. code-block:: c
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int MPI_Scatter(void *sendbuf, int sendcount, MPI_Datatype sendtype,
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void *recvbuf, int recvcount, MPI_Datatype recvtype, int root,
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MPI_Comm comm)
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.. code-block:: c
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int MPI_Gather(void *sendbuf, int sendcount, MPI_Datatype sendtype,
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void *recvbuf, int recvcount, MPI_Datatype recvtype, int root,
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MPI_Comm comm)
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In order to scatter a (contiguos) multidimensional array across all nodes, apply some operations to it and gather it back on the master one requires several lines of relatively complex code.
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The leading dimension of the array needs to be sliced, slice length and adress of the first element of each slice have to be computed and finally the MPI C API function has to be called.
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This can be packaged in the library once and for all.
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Using the library these operations look as follows:
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.. code-block:: c
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triqs::arrays::array<int, 3> A(8, 8, 8); // a three-dimensional array
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triqs::mpi::scatter(A);
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//do something with the corresponding part of A on each node
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triqs::mpi::gather(A);
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All index computations are encapsulated in the triqs::mpi library calls.
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In principle, the Boost.MPI library provides a similar interface for basic types and standard library containers. Transmission of the data however requires serialization and doubles the required memory. This poses a severe limitation when large amounts of data are to be transmitted.
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In this library, we employ metaprogramming techniques for type deduction as well as a lazy mechanism to avoid unecessary copyies of data.
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MPI reference
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----------------
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In this document, we describe the use of the TRIQS MPI library. For more information on MPI, see, e.g., the `open MPI web pages <http://www.open-mpi.org>`_ or consult the MPI reference manual. For more information on Boost.MPI, refer to the `Boost library documentation <http://www.boost.org>`_.
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Supported functions and types
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------------------------------
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Currently, the TRIQS MPI library supports the following operations::
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reduce
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allreduce
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broadcast
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scatter
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gather
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allgather
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These routines have the same meaning as their corresponding MPI analogues.
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They work for all 'basic' types, i.e. types for which a native MPI-type exists. These are::
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int
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long
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unsigned long
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double
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float
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std::complex<double>
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We also support ``std::vector<T>`` for ``T`` being a basic type, as well as the types provided by the TRIQS ``array`` and TRIQS ``gf`` libraries.
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In addition, the library provides a mechanism to enable MPI support for custom containers based on the array or gf libraries.
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Basic usage
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-------------
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The syntax is inspired by Boost.MPI. In order to create an MPI environment, set up the communicator and broadcast a variable, use the following code block:
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.. code-block:: c
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int main(int argc, char* argv[]) {
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mpi::environment env(argc, argv);
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mpi::communicator world;
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int a = 5;
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broadcast(a, world);
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}
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The declaration of the communicator is optional. If no communicator is passed to the routine, ``MPI_COMM_WORLD`` is used by default.
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All collective operations have the same signature. They take up to three arguments:
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.. code-block:: c
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reduce(T const &x, communicator = {}, int root = 0)
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Here T can be any supported type. The communicator is optional. By default, the data will be collected on (or transmitted from) the process with id 0.
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Headers
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--------------
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Support for basic types is provided by the header ``triqs/mpi/base.hpp`` and for vectors and arrays by ``triqs/mpi/vector.hpp`` and ``tiqs/mpi/array.hpp``. For custom container types, the header ``triqs/mpi/generic.hpp`` is required. Support for Boost.MPI is provided by the ``triqs/mpi/boost.hpp`` header file.
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For convenience, we provide the header::
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triqs/mpi.hpp
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which includes the headers for basic, vector, array and generic type support.
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Doxygen documentation
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-------------------------
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The :doxy:`full C++ parameter documentation<triqs::utility::parameters>` and
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the :doxy:`parameter_defaults documentation<triqs::utility::parameter_defaults>` are available here.
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MPI example
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-------------
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.. triqs_example:: ./mpi_0.cpp
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Simple MPI example.
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30
doc/reference/c++/mpi/mpi_0.cpp
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doc/reference/c++/mpi/mpi_0.cpp
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#include <triqs/arrays.hpp>
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#include <triqs/mpi.hpp>
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#include <iostream>
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using namespace triqs;
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using namespace triqs::arrays;
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using namespace triqs::mpi;
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int main(int argc, char *argv[]) {
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mpi::environment env(argc, argv);
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mpi::communicator world;
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int a = 5;
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broadcast(a);
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reduce_in_place(a);
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array<int, 2> A(2,10); A()=1;
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std::cout<<A<<std::endl;
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scatter(A);
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A += world.rank();
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gather(A);
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std::cout<<A<<std::endl;
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return 0;
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}
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78
test/triqs/mpi/mpi_vector.cpp
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test/triqs/mpi/mpi_vector.cpp
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/*******************************************************************************
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*
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* TRIQS: a Toolbox for Research in Interacting Quantum Systems
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*
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* Copyright (C) 2013 by O. Parcollet
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*
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* TRIQS is free software: you can redistribute it and/or modify it under the
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* terms of the GNU General Public License as published by the Free Software
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* Foundation, either version 3 of the License, or (at your option) any later
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* version.
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*
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* TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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* details.
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*
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* You should have received a copy of the GNU General Public License along with
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* TRIQS. If not, see <http://www.gnu.org/licenses/>.
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*
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******************************************************************************/
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#include <iostream>
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#include <type_traits>
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#include <triqs/arrays.hpp>
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#include <triqs/mpi.hpp>
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#include <iostream>
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#include <fstream>
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#include <sstream>
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using namespace triqs;
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using namespace triqs::arrays;
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using namespace triqs::mpi;
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template <typename T>
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std::ostream & operator << (std::ostream & out, std::vector<T> const & v) {
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for (size_t i =0; i<v.size(); ++i) out<< v[i];
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return out;
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}
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int main(int argc, char* argv[]) {
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mpi::environment env(argc, argv);
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mpi::communicator world;
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// using ARR = array<double,2>;
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using VEC = std::vector<std::complex<double>>;
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VEC A(7), B(7), AA(7);
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clef::placeholder<0> i_;
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clef::placeholder<1> j_;
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triqs::clef::make_expr(A)[i_] << i_+1;
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B = mpi::scatter(A, world);
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VEC C = mpi::scatter(A, world);
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std::ofstream out("node" + std::to_string(world.rank()));
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out << " A = " << A << std::endl;
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out << " B = " << B << std::endl;
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out << " C = " << C << std::endl;
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for(auto &x: B) x *= -1;
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for(auto &x: AA) x = 0;
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AA = mpi::gather(B, world);
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out << " AA = " << AA << std::endl;
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mpi::broadcast(AA, world);
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out << " cast AA = " << AA << std::endl;
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for (auto &x : AA) x = 0;
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AA = mpi::allgather(B, world);
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out << " AA = " << AA << std::endl;
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}
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@ -116,7 +116,7 @@ namespace mpi {
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static T invoke(tag::allreduce, communicator c, T a, int root) {
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static T invoke(tag::allreduce, communicator c, T a, int root) {
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T b;
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T b;
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MPI_Allreduce(&a, &b, 1, D(), MPI_SUM, root, c.get());
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MPI_Allreduce(&a, &b, 1, D(), MPI_SUM, c.get());
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return b;
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return b;
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}
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}
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}
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}
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// -----------
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// -----------
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static void broadcast(communicator c, std::vector<T> &a, int root) { MPI_Bcast(a.data(), a.size(), D(), root, c.get()); }
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static void broadcast(communicator c, std::vector<T> &a, int root) {
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size_t s=a.size();
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MPI_Bcast(&s, 1, mpi_datatype<size_t>::invoke(), root, c.get());
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if(c.rank() != root) a.resize(s);
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MPI_Bcast(a.data(), a.size(), D(), root, c.get()); }
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// -----------
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// -----------
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static std::vector<T> invoke(tag::reduce, communicator c, T const &a, int root) {
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static std::vector<T> invoke(tag::reduce, communicator c, T const &a, int root) {
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// -----------
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// -----------
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static std::vector<T> invoke(tag::allgather, communicator c, std::vector<T> const &a, int root) {
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static std::vector<T> invoke(tag::allgather, communicator c, std::vector<T> const &a, int root) {
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long size = reduce(a.size(), c, root);
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long size = allreduce(a.size(), c, root);
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std::vector<T> b(size);
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std::vector<T> b(size);
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auto recvcounts = std::vector<int>(c.size());
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auto recvcounts = std::vector<int>(c.size());
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